Computational systems and methods for disambiguating search terms corresponding to network members

ABSTRACT

Methods, apparatuses, computer program products, devices and systems are described that carry out accepting at least one search term corresponding to at least one member of a network; disambiguating the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data.

TECHNICAL FIELD

This description relates to data capture and data handling techniques.

SUMMARY

An embodiment provides a system. In one implementation, the system includes but is not limited to circuitry for accepting at least one search term corresponding to at least one member of a network; circuitry for disambiguating the at least one search term including circuitry for associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and circuitry for presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.

In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein-referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein-referenced method aspects depending upon the design choices of the system designer.

In one or more various aspects, related systems include but are not limited to computing means and/or programming for effecting the herein-referenced method aspects; the computing means and/or programming may be virtually any combination of hardware, software, and/or firmware configured to effect the herein-referenced method aspects depending upon the design choices of the system designer.

An embodiment provides a computer-implemented method. In one implementation, the method includes but is not limited to accepting at least one search term corresponding to at least one member of a network; disambiguating the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

An embodiment provides an article of manufacture including a computer program product. In one implementation, the article of manufacture includes but is not limited to a signal-bearing medium configured by one or more instructions related to (a) accepting at least one search term corresponding to at least one member of a network; (b) disambiguating the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and (c) presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.

An embodiment provides a system. In one implementation, the system includes but is not limited to a computing device and instructions. The instructions when executed on the computing device cause the computing device to (a) accept at least one search term corresponding to at least one member of a network; (b) disambiguate the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and (c) present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.

In addition to the foregoing, various other method and/or system and/or program product aspects are set forth and described in the teachings such as text (e.g., claims and/or detailed description) and/or drawings of the present disclosure.

The foregoing is a summary and thus may contain simplifications, generalizations, inclusions, and/or omissions of detail; consequently, those skilled in the art will appreciate that the summary is illustrative only and is NOT intended to be in any way limiting. Other aspects, features, and advantages of the devices and/or processes and/or other subject matter described herein will become apparent in the teachings set forth herein.

BRIEF DESCRIPTION OF THE FIGURES

With reference now to FIG. 1, shown is an example of a system for linking users of devices in which embodiments may be implemented, perhaps in a device and/or through a network, which may serve as a context for introducing one or more processes and/or devices described herein.

FIG. 2 illustrates certain alternative embodiments of the system for linking users of devices of FIG. 1.

With reference now to FIG. 3, shown is an example of an operational flow representing example operations related to linking users of devices, which may serve as a context for introducing one or more processes and/or devices described herein.

FIG. 4 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 5 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 6 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 7 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 8 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 9 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 10 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 11 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 12 illustrates an alternative embodiment of the example operational flow of FIG. 3.

FIG. 13 illustrates an alternative embodiment of the example operational flow of FIG. 3.

With reference now to FIG. 14, shown is a partial view of an example article of manufacture including a computer program product that includes a computer program for executing a computer process on a computing device related to linking users of devices, which may serve as a context for introducing one or more processes and/or devices described herein.

With reference now to FIG. 15, shown is an example device in which embodiments may be implemented related to linking users of devices, which may serve as a context for introducing one or more processes and/or devices described herein.

FIG. 16 illustrates an alternative embodiment of the example operational flow of FIG. 3.

With reference now to FIG. 17, shown is an example of a system for disambiguating search terms corresponding to network members in which embodiments may be implemented, perhaps in a device and/or through a network, which may serve as a context for introducing one or more processes and/or devices described herein.

With reference now to FIG. 18, shown is an example of an operational flow representing example operations related to disambiguating search terms corresponding to network members, which may serve as a context for introducing one or more processes and/or devices described herein.

FIG. 19 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 20 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 21 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 22 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 23 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 24 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 25 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 26 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 27 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 28 illustrates an alternative embodiment of the example operational flow of FIG. 18.

FIG. 29 illustrates an alternative embodiment of the example operational flow of FIG. 18.

With reference now to FIG. 30, shown is a partial view of an example article of manufacture including a computer program product that includes a computer program for executing a computer process on a computing device related to disambiguating search terms corresponding to network members, which may serve as a context for introducing one or more processes and/or devices described herein.

With reference now to FIG. 31, shown is an example device in which embodiments may be implemented related to disambiguating search terms corresponding to network members, which may serve as a context for introducing one or more processes and/or devices described herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.

FIG. 1 illustrates an example system 100 in which embodiments may be implemented. The system 100 includes a unique identifier unit 102. The unique identifier unit 102 may contain, for example, device-identifier acceptor module 104 and network-participation identifier acceptor module 106. Unique identifier unit 102 may communicate over a network or directly with device-identifier custodian 110 to accept device-identifier data 108. Unique identifier unit 102 may also communicate over a network or directly with network-participation identifier custodian 114 to accept network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device. Optionally, unique identifier unit 102 may also accept geodata 116 or financial account data 118. Unique identifier unit 102 may also include identity prediction module 122 for associating network-participation identifier data with a verified real-world user 120 associated with a communication device.

In FIG. 1, unique identifier unit 102 may assign a unique identifier based on accepted device-identifier data 108 and accepted network-participation identifier data 112. Optionally, unique identifier unit 102 may assign geodata 116 and/or financial account data 118 to an assigned unique identifier.

In FIG. 1, the unique identifier unit 102 is illustrated as possibly being included within a system 100. Of course, virtually any kind of computing device may be used to implement the special purpose unique identifier unit 102, such as, for example, a workstation, a desktop computer, a networked computer, a server, a collection of servers and/or databases, a virtual machine running inside a computing device, a mobile computing device, or a tablet PC.

Additionally, not all of the unique identifier unit 102 need be implemented on a single computing device. For example, the unique identifier unit 102 may be implemented and/or operable on a remote computer, while a user interface and/or local instance of the unique identifier unit 102 are implemented and/or occur on a local computer. Further, aspects of the unique identifier unit 102 may be implemented in different combinations and implementations than that shown in FIG. 1. For example, functionality of a user interface may be incorporated into the unique identifier unit 102. The unique identifier unit 120 may perform simple data relay functions and/or complex data analysis, including, for example, fuzzy logic and/or traditional logic steps. Further, many methods of assigning unique identifiers described herein or known in the art may be used, including, for example, algorithms used in generating globally unique identifiers, universally unique identifiers, other random number generation methods. In some embodiments, the unique identifier unit 102 may assign unique identifiers based on device-identifier data 108 and/or network-participation identifier data 112 available as updates through a network.

Unique identifier unit 102 may access data stored in virtually any type of memory that is able to store and/or provide access to information in, for example, a one-to-many, many-to-one, and/or many-to-many relationship. Such a memory may include, for example, a relational database and/or an object-oriented database, examples of which are provided in more detail herein.

FIG. 2 illustrates certain alternative embodiments of the system 100 of FIG. 1. In FIG. 2, The unique identifier unit 102 may also include unique identifier compiler logic 238 and or encryption protocol logic 240. Unique identifier unit 102 may communicate over a network or directly with device-identifier custodian 110 to accept device-identifier data 108, perhaps in the form of device identifier 230 from communication device 228. Unique identifier unit 102 may also communicate over a network or directly with network-participation identifier custodian 114 to accept network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device, perhaps from social network 232, business network 234, and/or other network 236.

In this way, the unique identifier unit 102 may generate a compiled and/or encrypted list of unique identifiers that are optionally coded with or otherwise linked to geodata and/or financial account data.

In some embodiments, unique identifier compiler logic 112 may create a compiled set of composite identifiers that can be used to disambiguate search results in the network based on device-identifier data, network participation identifier data, and/or geodata, for example. Unique identifier unit 102 can be operated by a telecom company or by a social or other network owner, or by both in cooperation with each other. A compiled list of unique identifiers as discussed herein can represent all or substantially all unique user devices in a given social network or other communications network, e.g., wireless network, email network, or the like.

A directory of uniquely-identified devices can serve as the foundation for searching within a social network, and for facilitating financial transactions via the device for members of the social network associated with the device.

In some embodiments, unique identifier unit 102 may also include identity prediction module 122 for associating network-participation identifier data with a verified real-world user 120 associated with a communication device 228. Identity prediction module 122 may include various search and/or matching functions for associating network-participation identifier data 112 with a verified real-world user 120 associated with a communications device 228. For example, identity prediction module 122 may include de-anonymization module 244, which in turn may include real-name profiling module 246. Identity prediction module 122 may also include web history tracking module 248, media content tracking module 250, and/or app list tracking module 252.

For the purposes of this application, SIM as used herein includes mini-SIM, micro-SIM, Universal Subscriber Identity Module, CDMA Subscriber Identity Module, Universal Integrated Circuit Card, Removable User Identity Module, virtual SIM, and other variants of the subscriber identity module described herein and understood by those of ordinary skill in the art.

As referenced herein, the unique identifier unit 102 may be used to perform various data querying and/or recall techniques with respect to the device-identifier data 108 and/or network-participation identifier data 112, in order to assign a unique identifier. For example, where the network-participation identifier data 112 is organized, keyed to, and/or otherwise accessible using one or more user accounts such as social network, email, or the like, unique identifier unit 102 may employ various Boolean, statistical, and/or semi-boolean searching techniques to assign a unique identifier. Similarly, for example, where device-identifier data 108 is organized, keyed to, and/or otherwise accessible using one or more device-identifier custodian 110, various Boolean, statistical, and/or semi-boolean searching techniques may be performed by unique identifier unit 102 to assign a unique identifier.

Many examples of databases and database structures may be used in connection with the unique identifier unit 102. Such examples include hierarchical models (in which data is organized in a tree and/or parent-child node structure), network models (based on set theory, and in which multi-parent structures per child node are supported), or object/relational models (combining the relational model with the object-oriented model).

Still other examples include various types of eXtensible Mark-up Language (XML) databases. For example, a database may be included that holds data in some format other than XML, but that is associated with an XML interface for accessing the database using XML. As another example, a database may store XML data directly. Additionally, or alternatively, virtually any semi-structured database may be used, so that context may be provided to/associated with stored data elements (either encoded with the data elements, or encoded externally to the data elements), so that data storage and/or access may be facilitated.

Such databases, and/or other memory storage techniques, may be written and/or implemented using various programming or coding languages. For example, object-oriented database management systems may be written in programming languages such as, for example, C++ or Java. Relational and/or object/relational models may make use of database languages, such as, for example, the structured query language (SQL), which may be used, for example, for interactive queries for disambiguating information and/or for gathering and/or compiling data from the relational database(s).

For example, SQL or SQL-like operations over one or more device-identifier data 108 and/or network-participation identifier data 112 may be performed, or Boolean operations using a device-identifier data 108 and/or network-participation identifier data 112 may be performed. For example, weighted Boolean operations may be performed in which different weights or priorities are assigned to one or more of the device-identifier data 108 and/or network-participation identifier data 112, including various network participation aliases associated with a particular verified real-world user, perhaps relative to one another. For example, a number-weighted, exclusive-OR operation may be performed to request specific weightings of network participation identifiers.

Following are a series of flowcharts depicting implementations. For ease of understanding, the flowcharts are organized such that the initial flowcharts present implementations via an example implementation and thereafter the following flowcharts present alternate implementations and/or expansions of the initial flowchart(s) as either sub-component operations or additional component operations building on one or more earlier-presented flowcharts. Those having skill in the art will appreciate that the style of presentation utilized herein (e.g., beginning with a presentation of a flowchart presenting an example implementation and thereafter providing additions to and/or further details in subsequent flowcharts) generally allows for a rapid and easy understanding of the various process implementations. In addition, those skilled in the art will further appreciate that the style of presentation used herein also lends itself well to modular and/or object-oriented program design paradigms.

FIG. 3 illustrates an operational flow 300 representing example operations related to linking users of devices. In FIG. 3 and in following figures that include various examples of operational flows, discussion and explanation may be provided with respect to the above-described system environments of FIGS. 1-2, and/or with respect to other examples and contexts. However, it should be understood that the operational flows may be executed in a number of other environments and contexts including that of FIG. 15, and/or in modified versions of FIGS. 1-2. Also, although the various operational flows are presented in the sequence(s) illustrated, it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently.

After a start operation, operation 310 depicts accepting device-identifier data corresponding to at least one communication device. For example, unique identifier unit 102 and/or device-identifier acceptor module 104 can accept device-identifier data 108 from a telecommunications carrier 220, for example in the form of a Unique Device Identifier (UDID) for an iPhone or iPod Touch. The UDID is a sequence of 40 letters and numbers that is specific to each iPhone or iPod Touch. It may look something like this: 2b6f0cc904d137be2e1730235f5664094b831186. Other examples of sources of device-identifier data 108 include voice-over-internet-protocol service providers such as Skype (peer-to-peer VoIP), and wireless carriers such as Verizon Wireless (CDMA-based wireless communication). Other examples of device-identifier data 108 include Media Access Control addresses (MAC address) and International Mobile Equipment Identity numbers (IMEI).

Operation 320 depicts accepting network-participation identifier data associated with a verified real-world user associated with the at least one communication device. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept from Facebook a username associated with a verified real-world user having an iPhone and corresponding account with a telecommunications company. In another example, unique identifier unit 102 may accept from LinkedIn the name of a person associated with a videoconferencing device and corresponding account with a videoconferencing service such as WebEx Web conferencing. In another example, unique identifier unit 102 may accept from Google the email address of a person associated with an Android phone and corresponding account with a wireless carrier.

In some embodiments, network-participation identifier custodian 114 and device-identifier custodian will cooperate to provide the necessary network-participation identifier data 112 and device-identifier data 108 to unique identifier unit 108. For example, Facebook may provide usernames, images, birthdates, telephone numbers, or other data that it has about the verified real-world users of its social network to a consortium of telecommunications carriers 220 (this may optionally involve an opting-in step whereby users of Facebook affirmatively approve this action), who may provide device-identifier data 108. Assigning a unique identifier (discussed below) for each network-user-associated device across each of the carriers in the consortium may result in a directory that is particularly valuable for the telecommunications carriers, who can then provide directory searching, support, and disambiguation for a potentially large fraction of the Facebook social network. Such a directory will likely be of equal interest and value to networks including Facebook in this example, for the same reasons. For example, a cross-carrier directory of Facebook members with associated phone numbers would be an added feature for Facebook that could significantly enhance the social information provided by the network.

Operation 330 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data. For example, unique identifier unit 102, upon accepting device-identifier data 108 and network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device, may assign a randomly-generated 32-bit unique identifier. In the iPhone example above, unique identifier unit 102 may accept the iPhone's unique device identifier (UDID) as the device-identifier data 108, accept an iTunes username associated with a user with a valid credit card and security code as the network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device, and assign a unique identifier to the device and username pair.

As another example, unique identifier unit 102 may accept the MAC address of a networked computer as the device-identifier data 108, accept an Outlook email address associated with a user with a verified biometric measurement as the network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device, and assign a unique identifier to the computer and email address pair.

As another example, unique identifier unit 102 may accept a mobile phone's integrated circuit card ID (ICC-ID) as the device-identifier data 108, accept a Facebook username associated with a user with a valid Facebook Credits account as the network-participation identifier data 112 associated with a verified real-world user 120 associated with the at least one communication device, and assign a unique identifier to the mobile phone and Facebook username pair.

In some embodiments, unique identifier unit 102 may include an identity prediction algorithm such as a de-anonymization algorithm, a real-name profiling algorithm, a web history tracking algorithm, media content tracking algorithm, and/or an app list tracking algorithm. These algorithms may aid in the association of network-participation identifier data with a verified real-world user 120 associated with the communication device 228, where those associations are not provided directly by a device-identifier custodian 110 and/or a network-participation identifier custodian 114.

FIG. 4 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 4 illustrates example embodiments where the accepting operation 310 may include at least one additional operation. Additional operations may include operation 400, 402, 404, and/or operation 406.

Operation 400 depicts accepting device-identifier data corresponding to at least one of a mobile phone, a wired telephone, a voice-over-internet-protocol telephone, a tablet computer, a notebook computer, a laptop computer, a desktop computer, or a networked television. For example, unique identifier unit 102 and/or device-identifier acceptor module 104 may accept device-identifier data corresponding to at least one of a mobile phone, a wired telephone, a voice-over-internet-protocol telephone, a tablet computer, a notebook computer, a laptop computer, a desktop computer, or a networked television. For example, device-identifier acceptor module 104 may accept a mobile phone's mobile equipment identifier, a land line's telephone number, or a networked computer's media access control address (MAC address) or internet protocol address (IP address).

Device-identifier data 108 may be accepted in different forms depending on the device identified. For example, an IP address or MAC address may be used to identify a computer.

Every device connected to the public internet is assigned a unique number known as an internet protocol address (IP address). IP addresses consist of four numbers separated by periods (also called a “dotted-quad”) and look something like 127.0.0.1. Since these numbers are usually assigned to internet service providers within region-based blocks, an IP address can often be used to identify the region or country from which a computer is connecting to the Internet. An IP address can sometimes be used to show the user's general location. An IP address may also be assigned to a Host name, which may be easier to remember. Hostnames may be looked up to find IP addresses, and vice-versa. At one time internet service providers issued one IP address to each user. These are static IP addresses. With the increased number of issued IP addresses, internet service providers now issue IP addresses in a dynamic fashion out of a pool of IP addresses using dynamic host configuration protocol (DHCP), which provides a central database for keeping track of computers that have been connected to the network. This prevents two computers from accidentally being configured with the same IP address. These are referred to as dynamic IP addresses. In addition to users connecting to the internet, with virtual hosting, a single machine can act like multiple machines, with multiple domain names and IP addresses.

MAC addresses are unique identifiers assigned to network interfaces for communications on the physical network segment. They are most often assigned by the manufacturer of a network interface card (NIC) and are stored in its hardware, the card's read-only memory, or some other firmware mechanism. If assigned by the manufacturer, a MAC address usually encodes the manufacturer's registered identification number and may be referred to as the burned-in address. It may also be known as an Ethernet hardware address (EHA), hardware address, or physical address. A network node may have multiple NICs and will then have one unique MAC address per NIC.

A subscriber identity module or subscriber identification module (SIM) is an integrated circuit that securely stores the service-subscriber key or international mobile subscriber identity (IMSI) used to identify a subscriber on mobile telephony devices (such as mobile phones and computers). A SIM card typically contains its unique serial number (integrated circuit card identifier or ICCID), an internationally unique number of the mobile user (IMSI), security authentication and ciphering information, temporary information related to the local network, a list of the services the user has access to and two passwords: a personal identification number (PIN) for usual use and a PIN unlock code (PUC) for unlocking. A SIM card may also store other carrier-specific data such as the SMSC (Short Message Service Center) number, Service Provider Name (SPN), Service Dialing Numbers (SDN), Advice-Of-Charge parameters and Value Added Service (VAS) applications.

A SIM card's ICCID is stored in the SIM card and also engraved or printed on the SIM card body. The ICCID is typically composed of an issuer identification number (IIN), an individual account identification number, and a check digit.

SIM cards are identified on their individual operator networks by a unique international mobile subscriber identity number or IMSI. Mobile operators connect mobile phone calls and communicate with their market SIM cards using their IMSIs. The format is: the first 3 digits represent the Mobile Country Code (MCC), the next 2 or 3 digits represent the Mobile Network Code (MNC), and the next digits represent the mobile station identification number.

SIM cards may also orthogonally store a number of SMS messages and phone book contacts. A SIM is held on a removable SIM card, which can be transferred between different mobile devices.

Operation 402 depicts accepting telephony device-identifier data including a telephone number associated with the telephony device. For example, unique identifier unit 102 may accept a ten-digit telephone number or a seven-digit telephone number from a telecommunications carrier 220 as the device-identifier data 108. The number contains the information necessary to identify uniquely the intended endpoint for the telephone call. Each such endpoint must have a unique number within the public switched telephone network.

Operation 404 depicts accepting at least one of subscriber identity module data or integrated circuit card identifier data corresponding to at least one communication device. For example, unique identifier unit 102 may accept an international mobile subscriber identity (IMSI) from a mobile phone's SIM card from a telecommunications carrier 220 as the device-identifier data 108. As another example, device-identifier acceptor module 104 may accept from a wireless communications service 222 an integrated circuit card identifier number from a SIM card for a mobile phone.

Operation 406 depicts accepting mobile equipment identifier data corresponding to at least one communication device. For example, unique identifier unit 102 may accept a mobile equipment identifier corresponding to a mobile handset from a telecommunications carrier 220 or wireless communications service 222. A Mobile Equipment IDentifier (MEID) is a globally unique 56-bit identification number for a physical piece of mobile equipment. Equipment identifiers are “burned” into a device and are used as a means to facilitate mobile equipment identification and tracking. Additionally, MEIDs are coordinated with International Mobile Equipment Identifiers (IMEIs), facilitating global roaming and harmonization between 3G technologies as a universal mobile equipment identifier. The MEID is a 14-digit hexadecimal value. The MEID is capable of being transmitted over the air upon a request from the network. The MEID is composed mainly of two basic components, the manufacturer code and the serial number.

FIG. 5 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 5 illustrates example embodiments where the accepting operation 310 may include at least one additional operation. Additional operations may include operation 500, 502, and/or operation 504.

Operation 500 depicts accepting international mobile subscriber identity data corresponding to at least one communication device. For example, device-identifier acceptor module 104 may accept an international mobile subscriber identity (IMSI) from a mobile phone's SIM card from a wireless communications service 222 as the device-identifier data 108. An International Mobile Subscriber Identity or IMSI is a unique identification associated with all GSM and UMTS network mobile phone users. It is stored as a 64-bit field in the SIM inside the phone and is sent by the phone to the network. It is also used for acquiring other details of the mobile device in the Home Location Register (HLR) or as locally copied in the Visitor Location Register. To prevent eavesdroppers identifying and tracking the subscriber on the radio interface, the IMSI is sent as rarely as possible and a randomly-generated temporary mobile subscriber identity (TMSI) is sent instead. The IMSI is used in any mobile network that interconnects with other networks. This number is kept in the phone directly or in the removable user identity module (R-UIM) card, a card developed for CDMA handsets that extends the GSM SIM card to CDMA phones and networks.

Operation 502 depicts accepting electronic serial number data corresponding to at least one communication device. For example, unique identifier unit 102 may accept an electronic serial number from a mobile phone's SIM card from a telecommunications carrier 220 as the device-identifier data 108. As another example, device-identifier acceptor module 104 may accept from a wireless communications service 222 an electronic serial number from a SIM card for a CDMA-based mobile phone.

Operation 504 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account. For example, unique identifier unit 102 may accept a mobile equipment identifier from a mobile phone's SIM card from a telecommunications carrier 220, the MEID corresponding to a billing account for a subscriber of a wireless service provided by the telecommunications carrier 220. As another example, device-identifier acceptor module 104 may accept from a wireless communications service 222 an IMSI from a SIM card for a mobile phone, the IMSI corresponding to a billing account for a subscriber of the wireless communications service 222.

FIG. 6 illustrates alternative embodiments of the example operational flow 300 of FIGS. 3 and 5. FIG. 6 illustrates example embodiments where the accepting operation 504 may include at least one additional operation. Additional operations may include operation 600, 602, and/or operation 604.

Operation 600 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a cable telecommunications billing account. For example, unique identifier unit 102 may accept a computer user's MAC address or IP address as the device identifier data 108. In this example, the MAC address or IP address of the computer may be linked to a Skype account for billing purposes.

Operation 602 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a wireless telecommunications billing account. For example, unique identifier unit 102 may accept from a wireless service provider an IMEI for a mobile phone linked to a billing account for an individual subscriber.

Operation 604 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one wireless telecommunications billing account comprises a satellite telecommunications billing account. For example, unique identifier unit 102 may accept from a satellite-based wireless service provider such as LightSquared, a device-identifier for a mobile phone linked to a billing account for an individual subscriber.

FIG. 7 illustrates alternative embodiments of the example operational flow 300 of FIGS. 3 and 5. FIG. 7 illustrates example embodiments where the accepting operation 504 may include at least one additional operation. Additional operations may include operation 700, 702, 704, and/or operation 706.

Operation 700 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a physical address. For example, device-identifier acceptor module 104 may accept from a wireless communications service 222 an IMSI from a SIM card for a mobile phone, the IMSI corresponding to a billing account for a subscriber of the wireless communications service 222 at a specific street, city, and country address.

Operation 702 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a bank account. For example, device-identifier acceptor module 104 may accept from a wireless communications service 222 an iPhone or iPod Touch device identifier, the identifier corresponding to a bank account number for a subscriber of the wireless service to the iPhone or iPod Touch device.

Operation 704 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises an electronic payment account. To continue the previous example involving the iPhone or iPod Touch device, the wireless service subscription may be linked to a bank's electronic payment service, wire transfer service, or the like.

Operation 706 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one electronic payment account, wherein the electronic payment account comprises at least one of a Google Checkout account, an Amazon Payments account, a PayPal account, or a mobile PayPal account. For example, a unique identifier unit 102 may accept a mobile device ID for an Android mobile phone from an Android app such as “Android Device ID” available for download from the Android Market. The Android mobile device ID, perhaps derived from a wireless network socket, for the mobile phone may correspond to a Google Checkout account for the subscriber of the wireless service to the mobile phone.

FIG. 8 illustrates alternative embodiments of the example operational flow 300 of FIGS. 3 and 5. FIG. 8 illustrates example embodiments where the accepting operation 504 may include at least one additional operation. Additional operations may include operation 800, 802, and/or operation 804.

Operation 800 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a credit card account. For example, a wireless device's service subscription may be linked to a user's credit card account.

Operation 802 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises a virtual account. For example, to continue the Google Checkout example above, a unique identifier unit 102 may accept a mobile device ID for an Android mobile phone from an Android app such as “Android Device ID” available for download from the Android Market. The Android mobile device ID for the mobile phone may correspond to a virtual account such as a Facebook credit account.

Operation 804 depicts accepting device-identifier data corresponding to at least one communication device that is linked to at least one virtual account, wherein the virtual account comprises at least one of a virtual wallet or a virtual prepaid credit card. For example, to continue the Google Checkout example above, a unique identifier unit 102 may accept a mobile device ID for an Android mobile phone from an Android app such as “Android Device ID” available for download from the Android Market. The Android mobile device ID for the mobile phone may correspond to a virtual wallet account such as Google wallet.

FIG. 9 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 9 illustrates example embodiments where the accepting operation 320 may include at least one additional operation. Additional operations may include operation 900, 902, 904, and/or operation 906.

Operation 900 depicts accepting network-participation identifier data associated with at least one of a user's social security number, a user's national identification card, a user's biometric measurement, a user's passport number, a user's tax identification number, a user's internet domain, or a user's authentication certificate. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept network-participation identifier data associated with at least one of a user's social security number, a user's national identification card, a user's biometric measurement, a user's passport number, a user's tax identification number, a user's interne domain, or a user's authentication certificate. For example, network-participation identifier acceptor module 106 may accept a Facebook username as network-participation identifier data, the username associated with a photograph of the user as a biometric measurement verifying that a real-world user is associated with the username. In some embodiments, an image recognition system may be employed to associate an image with a specific user. In some embodiments, the real-world user may be a corporation.

In another example, network-participation identifier acceptor module 106 may accept an email address as network-participation identifier data, the email address associated with a social security number on file with a telecommunications company with which the user has a service subscription.

As used herein, “network-participation identifier data” may refer to a signifier of belonging in a network, such as an email address; a username, such as a social networking user name; or other mark such as an image, number, or writing that signifies participation in a particular network.

Operation 902 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a Google+ username as a network-participation identifier datum, wherein the Google+ username is associated with a photograph of the user having the username. In some embodiments, the photograph of the user may be analyzed by image recognition technologies to identify a person having specific geographic, demographic, or other identifying characteristics.

Operation 904 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device, wherein the social networking data comprises at least one of a username, an @-tagged twitter handle, a corporate login, or a website uniform resource locator (URL). For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a blogger's website URL as a network-participation identifier datum, wherein the website URL is associated with a photograph and/or description of the blogger on the website at the website URL.

Operation 906 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device, wherein the social networking data comprises at least one of Facebook data, Twitter data, or LinkedIn data. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a LinkedIn username as a network-participation identifier datum, wherein the username is associated with a public profile of a user of the business-related social networking site LinkedIn.

FIG. 10 illustrates alternative embodiments of the example operational flow 300 of FIGS. 3 and 9. FIG. 10 illustrates example embodiments where the accepting operation 902 may include at least one additional operation. Additional operations may include operation 1000, 1002, and/or operation 1004.

Operation 1000 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device, wherein the social networking data comprises at least one of image data, constellation of social contact data, or user input data. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a list of social contacts from a social network such as Facebook or LinkedIn as the network-participation identifier data. In another example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a list of email contacts grouped as friends or family from an email contact list as the network-participation identifier data.

Operation 1002 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device, wherein the social networking data comprises data accumulated from multiple sources. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a plurality of usernames sourced from various social networks, each corresponding to the same verified real-world user of the at least one communication device as the data accumulated from multiple sources. As another example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a set of photographs of the same verified real-world user of the at least one communication device, sourced from various social networks as the data accumulated from multiple sources.

Operation 1004 depicts accepting social networking data corresponding to at least one verified real-world user of the at least one communication device, wherein the social networking data comprises at least one of data used to create additional data or data used to find additional data. For example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a website URL of a social networking site's videoconferencing or videochat feed as data (website URL) used to create additional data (streaming video of network participants). In another example, unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept a user image or alias that can be used to find other data, for example as a search term in an reverse-image query or a text query, respectively.

FIG. 11 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 11 illustrates example embodiments where the assigning operation 330 may include at least one additional operation. Additional operations may include operation 1100, 1102, and/or operation 1104.

Operation 1100 depicts assigning at least one of a multi-digit decimal number, a multi-digit hexadecimal number, or a randomized code as the unique identifier. For example, unique identifier unit 102 may assign at least one of a multi-digit decimal number, a multi-digit hexadecimal number, or a randomized code as the unique identifier. In another example, unique identifier unit 102 may assign a unique identifier using an algorithm(s) known in the art to generate unique multi-digit decimal numbers or unique multi-digit hexadecimal numbers. See, e.g., U.S. Pat. No. 8,010,587 (hereby incorporated by reference).

Operation 1102 depicts further comprising encrypting the unique identifier. For example, unique identifier unit 102 and/or encryption protocol logic 240 may encrypt the assigned unique identifier. Encrypting the unique identifier may be desirable in cases where telecommunications carriers sharing a directory comprised of unique identifiers for the purpose of locating and disambiguating users of one or more networks, can share the unique identifiers but still protect them and the underlying data from access by undesirable entities such as spammers and telemarketers. In another example, unique identifier unit 102 may encrypt the assigned identifier or associated sensitive personal and/or financial information according to encryption schemes described herein and known in the art. See, e.g., U.S. Pat. No. 8,010,791 and U.S. Pat. No. 8,010,786 (hereby incorporated by reference).

Operation 1104 depicts further comprising encrypting the unique identifier, wherein the encrypting the unique identifier includes performing at least one of symmetric key encryption, public key encryption, hybrid digital signature encryption, using a one-way hash function, using a random identifier, or using a pseudo-random identifier. For example, unique identifier unit 102 and/or encryption protocol logic 240 may encrypt the assigned unique identifier using a one-way hash function, which is easy to compute on every input, but hard to invert given the image of a random input.

FIG. 12 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 12 illustrates example embodiments where the assigning operation 330 may include at least one additional operation. Additional operations may include operation 1200, 1202, 1204, and/or operation 1206.

Operation 1200 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, further comprising assigning to the unique identifier geo-locator data from the at least one communication device. For example, unique identifier unit 102 may accept geodata 116 from a mobile phone, and then assign that geodata to an assigned unique identifier corresponding to a device and a network participant. In another example, unique identifier unit 102 may accept geodata 116 in the form of a computer's IP address, and then assign that geodata to an assigned unique identifier corresponding to the computer and a verified network participant associated with that computer.

Operation 1202 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, further comprising assigning to the unique identifier geo-locator data from the at least one communication device, wherein the geo-locator data is assigned via a global positioning satellite function of the communication device. For example, unique identifier unit 102 may accept geodata 116 from a mobile phone having a gps receiver, and then assign that geodata to an assigned unique identifier corresponding to a device and a network participant.

Operation 1204 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, further comprising assigning to the unique identifier geo-locator data from the at least one communication device, wherein the geo-locator data is derived from at least one of cellular phone tower proximity, Wi-Fi use, user-entered location data, or proximity to at least one other device. For example, unique identifier unit 102 may accept geodata 116 from a smart phone using a Wi-Fi network contained in a database that contains location information for the Wi-Fi network, and then assign that geodata to an assigned unique identifier corresponding to a device and a network participant.

Operation 1206 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, further comprising assigning to the unique identifier geo-locator data from the at least one communication device, wherein the geo-locator data is derived from at least one of a detected vehicle use, a detected user activity, or a detected user location. For example, unique identifier unit 102 may derive geo-locator data from detected automobile use, based on, for example, last known location and predicted range of travel of the automobile. In another example, unique identifier unit 102 may receive or deduce geo-locator data from a detected user activity, for example, checking in with foursquare at a specific location or searching for driving directions in a web browser, respectively.

FIG. 13 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 13 illustrates example embodiments where the assigning operation 330 may include at least one additional operation. Additional operations may include operation 1300, 1302, 1304, and/or operation 1306.

Operation 1300 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents multiple communication devices associated with a single user. For example, unique identifier unit 102 may assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents multiple communication devices associated with a single user. In another example, unique identifier unit 102 may accept device-identifier data from a mobile phone, a desktop computer, and a laptop computer, each of which is associated with a single user, for example by virtue of an IMSI or other SIM data, email data, billing account data, or social networking data.

Operation 1302 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents a single communication device associated with multiple users. For example, unique identifier unit 102 may assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents a single communication device associated with multiple users. In another example, unique identifier unit 102 may accept device-identifier data from a mobile phone, the device-identifier data associated with a multiple users, for example members of a family by virtue of different login data used for access to the device and/or different social networking usernames used on the device.

Operation 1304 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents a single communication device associated with a single user. For example, unique identifier unit 102 may assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, wherein the unique identifier represents a single communication device associated with a single user. As another example, unique identifier unit 102 may assign a unique identifier at least partly based on a videoconferencing device ID, such as an IP address or a MAC address, and at least partly based on a username and password for the videoconference, accompanied by a video image of a user associated with the username and password, verifying that a real-world user is associated with the videoconferencing device.

Operation 1306 depicts assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, and then adding an assigned unique identifier to an inter-service-provider directory of unique identifiers. For example, unique identifier unit 102 may assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data, and then adding an assigned unique identifier to an inter-service-provider directory of unique identifiers. In another example, unique identifier unit 102 may assign a unique identifier at least partly based on SIM data identifying a user's mobile phone, and at least partly based on subscriber's participation in the wireless network, as verified, for example, by a social security number for the user on file with the wireless carrier for the mobile device, for example, Verizon. Verizon may similarly create unique identifiers for all of the other verified real-world users of its network and their associated devices. Other wireless carriers may similarly create unique identifiers for their subscribers and associated devices.

If many wireless carriers agree to share their unique identifier lists and keep them in the same format for use as a global directory of mobile phone users, a comprehensive “white pages” of communications device users becomes possible, across potentially all service providers. Such a directory could also be keyed to social networking data such as username or user image, such that, for example, Facebook users could easily find each other's device contact information and possibly location information. Inclusion of users' device information in such a directory could be done on an opt-in basis.

As used herein, a unique identifier based on a device-identifier and a network-participant identifier may be keyed to that underlying data. That is, having the unique identifier corresponding to specific device data and specific network-participation identifier data associated with a verified real-world user associated with the at least one communication device will permit the creator of the unique identifier to use it to call up the specific device data and specific network participation identifier data. This may allow, for example, a telecommunications carrier to disambiguate one user from another having similar or identical network participation identifier data. This can be done on the basis of different device identifier data for the two users with similar or identical network participation identifier data, for example.

FIG. 14 illustrates a partial view of an example article of manufacture 1400 that includes a computer program 1404 for executing a computer process on a computing device. An embodiment of the example article of manufacture 1400 is provided including a signal bearing medium 1402, and may include one or more instructions for accepting device-identifier data corresponding to at least one communication device; one or more instructions for accepting network-participation identifier data associated with a verified real-world user associated with the at least one communication device; and one or more instructions for assigning a unique identifier at least partly based on the device-identifier data and the network-participation identifier data. The one or more instructions may be, for example, computer executable and/or logic-implemented instructions. In one implementation, the signal-bearing medium 1402 may include a computer-readable medium 1406. In one implementation, the signal bearing medium 1402 may include a recordable medium 1408. In one implementation, the signal bearing medium 1402 may include a communications medium 1410.

FIG. 15 illustrates an example system 1500 in which embodiments may be implemented. The system 1500 includes a computing system environment. The system 1500 also illustrates a user 1512 using a device 1504, which is optionally shown as being in communication with a computing device 1502 by way of an optional coupling 1506. The optional coupling 1506 may represent a local, wide-area, or peer-to-peer network, or may represent a bus that is internal to a computing device (e.g., in example embodiments in which the computing device 1502 is contained in whole or in part within the device 1504). A storage medium 1508 may be any computer storage media. In one embodiment, the computing device 1502 may include a virtual machine operating within another computing device. In an alternative embodiment, the computing device 1502 may include a virtual machine operating within a program running on a remote server.

The computing device 1502 includes computer-executable instructions 1510 that when executed on the computing device 1502 cause the computing device 1502 to (a) accept device-identifier data corresponding to at least one communication device; (b) accept network-participation identifier data associated with a verified real-world user associated with the at least one communication device; and (c) assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data. As referenced above and as shown in FIG. 15, in some examples, the computing device 1502 may optionally be contained in whole or in part within the device 1504.

In FIG. 15, then, the system 1500 includes at least one computing device (e.g., 1502 and/or 1504). The computer-executable instructions 1510 may be executed on one or more of the at least one computing device. For example, the computing device 1502 may implement the computer-executable instructions 1510 and output a result to (and/or receive data from) the computing device 1504. Since the computing device 1502 may be wholly or partially contained within the computing device 1504, the device 1504 also may be said to execute some or all of the computer-executable instructions 1510, in order to be caused to perform or implement, for example, various ones of the techniques described herein, or other techniques.

The device 1504 may include, for example, a portable computing device, workstation, or desktop computing device. In another example embodiment, the computing device 1502 is operable to communicate with the device 1504 associated with the user 1512 to receive information about the input from the user 1512 for performing data access and data processing, and assign a unique identifier at least partly based on the device-identifier data and the network-participation identifier data.

FIG. 16 illustrates alternative embodiments of the example operational flow 300 of FIG. 3. FIG. 16 illustrates example embodiments where the accepting operation 320 may include at least one additional operation. Additional operations may include operation 1600, 1602, and/or operation 1604.

Operation 1600 depicts accepting network-participation identifier data associated with a verified real-world user associated with the at least one communication device, further comprising associating network-participation identifier data with a real-world user associated with the at least one communication device. To continue an example of operation 302 above in which unique identifier unit 102 and/or network-participation identifier acceptor module 106 may accept from Facebook a username associated with a verified real-world user having an iPhone and corresponding account with a telecommunications company, the unique identifier unit 102 and/or identity prediction module 122 may search one or more identity databases for associations between the username and a real-world user, and for associations between that real-world user and the iPhone. Sources of data for associating a user with network-participation data and/or a communication device may include, for example, information that is provided by the user. For example, social network, message boards, internet forums, and the like may contain a link between a username and a phone number, a real-world name, birth date, gender, age, or other identifying attribute. Private sources of data may also include information provided by the user, such as private social networks, ecommerce websites, or any websites to which a consumer provides sign-up information. Publicly available sources may contain unique consumer information, including for example, vehicle registration records, real estate records, driving records, voting records, political donations, health information, government related data, technographics, or any other on-line sources disclosing information about people. Examples of algorithms that may be employed to perform these associations can be found in U.S. Patent Application Publication 2010/0088313 “Data Source Attribution System,” hereby incorporated in its entirety by reference. See also U.S. Patent Application Publication 2010/0010993 “Distributed Personal Information Aggregator,” also hereby incorporated in its entirety by reference.

In the example above, the Facebook username may be used as a search query by identity prediction module 122 to find the same username on a blog containing a real-world name and mobile phone number associated with the username, the mobile phone number being assigned to the iPhone associated with the now-verified real-world user associated with the Facebook username.

Operation 1602 depicts associating network-participation identifier data with a real-world user associated with the at least one communication device, including at least one of performing the association using identity prediction, performing the association using de-anonymization, or performing the association using real-name profiling. For example, unique identifier unit 102, identity prediction module 122, de-anonymization module 244, and/or real-name profiling module 246 may associate network-participation identifier data with a real-world user associated with the at least one communication device, including at least one of performing the association using identity prediction, performing the association using de-anonymization, or performing the association using real-name profiling. For example, accept from LinkedIn the name of a person associated with a videoconferencing device and corresponding account with a videoconferencing service such as WebEx Web conferencing. If the association between the LinkedIn subscriber and a real-world user associated with the videoconferencing device is missing, identity prediction module 122 may search relevant identity databases for matches to the subscriber's username or other profile data. In this way, verification of the real-world user can be accomplished, and association between the network-participation identifier data and the user associated with the communications device can be performed.

Operation 1604 depicts associating network-participation identifier data with a real-world user associated with the at least one communication device, including at least one of performing the association using web history tracking, performing the association using media content tracking, or performing the association using app data tracking. For example, unique identifier unit 102, Web history tracking module 248, media content tracking module 250, and/or app data tracking module 252 may associate network-participation identifier data with a real-world user associated with the at least one communication device, including at least one of performing the association using web history tracking, performing the association using media content tracking, or performing the association using app data tracking. For example, unique identifier unit 102 may accept from Google the email address of a person associated with an Android phone and corresponding account with a wireless carrier. In this example, app data tracking module 252 may match the email address with device ID from the phone, e.g., SIM data, and make the association between the email address and the phone. Additionally, web history tracking module 248 may search public databases for verification that a real-world user is associated with the email address, for example by searching department of motor vehicle records or real estate records.

Disambiguating Search Terms Corresponding to Network Members

FIG. 17 illustrates an example system 1700 in which embodiments may be implemented. The system 1700 includes a disambiguation unit 1704. The disambiguation unit 1704 may contain, for example, primary query data acceptor module 1718, ancillary query data acceptor module 1710, and/or identity prediction module 122. disambiguation unit 1704 may communicate over a network or directly with user 1701 to accept primary query data 1702 and ancillary query data 1703. Disambiguation unit 1704 may also communicate over a network or directly with user 1701 to present disambiguated search term(s) 1730. Disambiguation unit 1704 may also include filter logic 1718, ranking logic 1720, correction logic 1722, subscriber database 1724, cross-carrier subscriber database 1726, and/or other database 1728.

In FIG. 17, disambiguation unit 1704 may associate at least one search term from user 1701 with ancillary query data 1703 such as network-participation identifier data or device-identifier data. Optionally, disambiguation unit 1704 may accept ancillary query data 1703 such as search locus data. Primary query data acceptor module 1718 may include name query acceptor module 1706 and/or number query acceptor module. Ancillary query data acceptor module 1710 may include device-identifier data acceptor module 1712, network-participation identifier data acceptor module 1714, and/or search locus data acceptor module 1716. Identity prediction module 122 may include de-anonymization module 244, real-name profiling module 246, web history tracking module 248, media content tracking module 250, and/or app data tracking module 252.

In FIG. 17, the disambiguation unit 1704 is illustrated as possibly being included within a system 1700. Of course, virtually any kind of computing device may be used to implement the special purpose disambiguation unit 1704, such as, for example, a workstation, a desktop computer, a networked computer, a server, a collection of servers and/or databases, a virtual machine running inside a computing device, a mobile computing device, or a tablet PC.

Additionally, not all of the disambiguation unit 1704 need be implemented on a single computing device. For example, the disambiguation unit 1704 may be implemented and/or operable on a remote computer, while a user interface and/or local instance of the disambiguation unit 1704 are implemented and/or occur on a local computer. Further, aspects of the disambiguation unit 1704 may be implemented in different combinations and implementations than that shown in FIG. 17. For example, functionality of a user interface may be incorporated into the disambiguation unit 1704. The disambiguation unit 1704 may perform simple data relay functions and/or complex data analysis, including, for example, fuzzy logic and/or traditional logic steps. Further, many methods of disambiguating search terms corresponding to network members described herein or known in the art may be used, including, for example, algorithms commonly used in disambiguating words based on syntax, context, and semantics. In some embodiments, the disambiguation unit 1704 may disambiguate a search term by associating the search term with network-participation identifier data and/or device-identifier data available as updates through a network.

Disambiguation unit 1704 may access data stored in virtually any type of memory that is able to store and/or provide access to information in, for example, a one-to-many, many-to-one, and/or many-to-many relationship. Such a memory may include, for example, a relational database and/or an object-oriented database, examples of which are provided in more detail herein.

As referenced herein, the disambiguation unit 1704 may be used to perform various data querying and/or recall techniques with respect to the primary query data 1702 and the ancillary query data 1703 (e.g., device-identifier data 108, searcher location data, and/or network-participation identifier data 112), in order to present a disambiguated search term(s) 1730. For example, where network-participation identifier data 112 is organized, keyed to, and/or otherwise accessible using one or more user accounts such as social network, email, or the like, disambiguation unit 1704 may employ various Boolean, statistical, and/or semi-Boolean searching techniques to associate a search term with network-participation identifier data and/or device-identifier data for disambiguation. Similarly, for example, where network member data is organized, keyed to, and/or otherwise accessible using one or more inter-carrier directory or cross-carrier subscriber database 1726, various Boolean, statistical, and/or semi-boolean searching techniques may be performed by disambiguation unit 1704 to disambiguate a search term(s).

Many examples of databases and database structures may be used in connection with the disambiguation unit 1704. Such examples include hierarchical models (in which data is organized in a tree and/or parent-child node structure), network models (based on set theory, and in which multi-parent structures per child node are supported), or object/relational models (combining the relational model with the object-oriented model).

Still other examples include various types of eXtensible Mark-up Language (XML) databases. For example, a database may be included that holds data in some format other than XML, but that is associated with an XML interface for accessing the database using XML. As another example, a database may store XML data directly. Additionally, or alternatively, virtually any semi-structured database may be used, so that context may be provided to/associated with stored data elements (either encoded with the data elements, or encoded externally to the data elements), so that data storage and/or access may be facilitated.

Such databases, and/or other memory storage techniques, may be written and/or implemented using various programming or coding languages. For example, object-oriented database management systems may be written in programming languages such as, for example, C++ or Java. Relational and/or object/relational models may make use of database languages, such as, for example, the structured query language (SQL), which may be used, for example, for interactive queries for disambiguating information and/or for gathering and/or compiling data from the relational database(s).

For example, SQL or SQL-like operations over one or more primary query data 1702 and/or ancillary query data 1703 may be performed, or Boolean operations using primary query data 1702 and/or ancillary query data 1703 may be performed. For example, weighted Boolean operations may be performed in which different weights or priorities are assigned to one or more of the primary query data 1702 and/or ancillary query data 1703, including various device identifiers, locations, and/or network participation aliases associated with a particular network member, perhaps relative to one another. For example, a number-weighted, exclusive-OR operation may be performed to request specific weightings of device and/or network participation identifiers.

Following are a series of flowcharts depicting implementations. For ease of understanding, the flowcharts are organized such that the initial flowcharts present implementations via an example implementation and thereafter the following flowcharts present alternate implementations and/or expansions of the initial flowchart(s) as either sub-component operations or additional component operations building on one or more earlier-presented flowcharts. Those having skill in the art will appreciate that the style of presentation utilized herein (e.g., beginning with a presentation of a flowchart presenting an example implementation and thereafter providing additions to and/or further details in subsequent flowcharts) generally allows for a rapid and easy understanding of the various process implementations. In addition, those skilled in the art will further appreciate that the style of presentation used herein also lends itself well to modular and/or object-oriented program design paradigms.

FIG. 18 illustrates an operational flow 1800 representing example operations related to disambiguating search terms corresponding to network members. In FIG. 18 and in following figures that include various examples of operational flows, discussion and explanation may be provided with respect to the above-described system environments of FIG. 17, and/or with respect to other examples and contexts. However, it should be understood that the operational flows may be executed in a number of other environments and contexts including that of FIG. 31, and/or in modified versions of FIG. 17. Also, although the various operational flows are presented in the sequence(s) illustrated, it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently.

After a start operation, operation 1810 depicts accepting at least one search term corresponding to at least one member of a network. For example, disambiguation unit 1704, name query acceptor module 1706, and/or number query acceptor module 1708 can accept primary query data 1702 from a user 1701, for example in the form of a proper name search term like “John Smith” or a username search term such as “jsmith23.” In another example, disambiguation unit 1704 or number query acceptor module 1708 may accept a street address search term from a user 1701, for example 123 Any Street, Peoria Ill.

Operation 1820 depicts disambiguating the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data. For example, disambiguation unit 1704, network-participation identifier data acceptor module 1714 and/or device-identifier data acceptor module 1712 may access ancillary query data 1703 from a social networking website such as Facebook, associate a search term, e.g., “John Smith” to continue the example from above, with information known or knowable about the searcher and/or the device that the searcher is using to conduct the query. One way of making an association between a search term and network participation data or device identification data may involve finding links between the search term and elements of the social network of user 1701. For example, a search for “John Smith” by a user 1701 logged in to Facebook may return results including phone numbers for those John Smiths that are connected in some way to the user 1701's Facebook friends network, perhaps ranked by degree of direct connection. Phone numbers for the John Smiths may be determined by the methods described herein or in the art for identity prediction, based on, for example, location of the searching user 1701 and/or publicly available records for the list of John Smiths. In another embodiment, disambiguation unit 1702 may query cross-carrier subscriber database 1726 (as described herein) containing telecommunication carrier subscriber device identification information across a social network for the search term in question to find a phone number for the John Smith of interest. In this way, a universal white pages for a social network may be created, for all telecommunications carriers, so that users may find phone numbers and other contact information, for example, for other people in their social network. Those people need not necessarily be directly or even indirectly connected to the user 1701 in the network.

In some embodiments, user 1701 will provide both primary query data 1702 and ancillary query data 1703 to disambiguation unit 1702. In another embodiment, user 1701 may provide primary query data 1702 and grant access to ancillary query data 1703, for example by an affirmative step of opting-in or by not opting-out of a request by disambiguation unit 1702. Ancillary query data may be obtainable as a matter of course by disambiguation unit 1702. For example, image data for a user 1701 providing videochat data to a search website, device identification data provided by an iPhone app, or location information provided by a user's interne protocol address may be among those data that are freely given or available to a computing device networked with a device upon which user 1701 has entered primary query data 1702. Other ancillary query data may be available from a social network that the user 1701 is logged in to. For example, Facebook may provide proper names, usernames, images, birthdates, telephone numbers, friend lists, or other data that it has about the users of its social network. Such information may be publicly available.

Operation 1830 depicts presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. For example, disambiguation unit 1702, upon making an association between a search term and ancillary data, for example, related to the user 1701, may present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data and ancillary data. For example, disambiguation unit 1702 may accept a search term such as Jane Doe from user 1701. Disambiguation unit 1702 may then note the internet protocol address of the device from which user 1701 is performing the search. Disambiguation unit 1702 may then determine a region in which the user 1701 is located, and then limit its search to known Jane Doe's in that region, or in another embodiment, to Jane Doe's associated with user 1701's social network.

As another example, disambiguation unit 1702 may detect device identifier data such as a mobile phone's integrated circuit card ID (ICC-ID) as the ancillary query data 1703 (for example, some iPhone apps send device ID data with other app data), and associate that device ID (or associated app data) with the primary query data to arrive at an association that allows for presentation of a disambiguated search term to the user 1701.

In some embodiments, disambiguation unit 1702 may include an identity prediction algorithm such as a de-anonymization algorithm, a real-name profiling algorithm, a web history tracking algorithm, media content tracking algorithm, and/or an app list tracking algorithm. These algorithms may aid in the association of ancillary query data with a search term, where those associations are not provided directly by a user 1701 or readily apparent to disambiguation unit 1702.

FIG. 19 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 19 illustrates example embodiments where the accepting operation 1810 may include at least one additional operation. Additional operations may include operation 1900, 1902, and/or operation 1904.

Operation 1900 depicts accepting a name corresponding to at least one member of a network. For example, disambiguation unit 1702 and/or name query acceptor module 1706 may accept a name corresponding to at least one member of a network. For example, name query acceptor module 1706 may accept as primary query data the name John Smith corresponding to a John Smith in a social network of the user, for example, LinkedIn. Names are typically entered by a user at a user interface, by typing, speaking, pasting, or other means of entering a name into a search window or search box, for example in a web browser.

Operation 1902 depicts accepting at least one of a first name, a last name, a nickname, a username, or a name including initials corresponding to at least one member of a network. For example, disambiguation unit 1702 and/or name query acceptor module 1706 may accept at least one of a first name, a last name, a nickname, a username, or a name including initials corresponding to at least one member of a network. For example, name query acceptor module 1706 may accept as primary query data the name J. J. Smith or Johnny Smith corresponding to a John Smith in a social network of the user, for example, Google+.

Operation 1904 depicts accepting at least one of a physical address, an email address, or a web address corresponding to at least one member of a network. For example, disambiguation unit 1702 and/or name query acceptor module 1706 may accept at least one of a physical address, an email address, or a web address corresponding to at least one member of a network. For example, name query acceptor module 1706 may accept as primary query data “jsmith123@hotmail.com” corresponding to a John Smith in a social network of the user, for example, Facebook.

FIG. 20 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 20 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2000, 2002, and/or operation 2004.

Operation 2000 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data corresponding to at least one of a mobile phone, a wired telephone, a voice-over-internet-protocol telephone, a tablet computer, a notebook computer, a laptop computer, a desktop computer, or a networked television. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate a search term with a mobile equipment identifier corresponding to a mobile handset as ancillary query data 1703 from a user 1701, telecommunications carrier 220, or wireless communications service 222. As discussed above, a Mobile Equipment IDentifier (MEID) is a globally unique 56-bit identification number for a physical piece of mobile equipment. Equipment identifiers are “burned” into a device and are used as a means to facilitate mobile equipment identification and tracking. Additionally, MEIDs are coordinated with International Mobile Equipment Identifiers (IMEIs), facilitating global roaming and harmonization between 3G technologies as a universal mobile equipment identifier. The MEID is a 14-digit hexadecimal value. The MEID is capable of being transmitted over the air upon a request from the network. The MEID is composed mainly of two basic components, the manufacturer code and the serial number. In some embodiments, associating primary query data 1702 with device identification data such as the MEID provides an avenue for disambiguation, because (1) disambiguation unit 1702 may find primary search term(s) on related devices on the same carrier service, or (2) by associating devices in the same geographic area as determined by the device ID with the primary search term(s).

Operation 2002 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including a telephone number associated with a telephony device. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate the at least one search term with device-identifier data including a telephone number associated with a telephony device. To continue the example discussed above, disambiguation unit 1702 may make the association between a primary search term and a device related (perhaps by carrier service or location) to the device of the user with a known MEID. In this case, the MEID may provide a link to a telephone number for its mobile device. This telephone number may in turn be used to find a telephone number for a John Smith as a search term, for example where an inter-service-provider directory of unique identifiers has been created, as discussed above.

Operation 2004 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including subscriber identity module data or integrated circuit card identifier data corresponding to at least one communication device. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate the at least one search term with device-identifier data including subscriber identity module data or integrated circuit card identifier data corresponding to at least one communication device. For example, accessing ICCI device-identifier data corresponding to the user 1701 may provide location data, network participation data (by virtue of a name associated with the device-identifier data), other elements that may be used to associate a search term with relevant information. One use for this disambiguation is to provide a universal white pages that can cross carrier boundaries and include most if not all members of a social network, for example.

FIG. 21 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 21 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2100, 2102, and/or operation 2104.

Operation 2100 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including mobile equipment identifier data corresponding to at least one communication device. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate the at least one search term with device-identifier data including mobile equipment identifier data corresponding to at least one communication device. In one embodiment, disambiguation unit 1702 may associate a search term with a Unique Device Identifier (UDID) for an iPhone upon which the user 1701 is performing the search. iPhone apps are known to transmit device ID information with other app data, thus this is something that may be accepted by disambiguation unit 1702 in course of receiving the primary query data 1702. Having iPhone device ID information allows disambiguation unit 1702 to make find additional information about the user 1701, in order to make associations between the device/network participation of the user 1701 and the member of a network that user 1701 is searching for.

Operation 2102 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including international mobile subscriber identity data corresponding to at least one communication device. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate the at least one search term with device-identifier data including international mobile subscriber identity data corresponding to at least one communication device. In one embodiment, disambiguation unit 1702 may associate a search term with an IMSI belonging to cell phone upon which the user 1701 is performing the search. Having the IMSI may provide disambiguation unit 1702 with location data, for example, country of origin data, with which to make associations between the user 1701 and the member of a network that user 1701 is searching for. For example, IMSI data indicating that the user 1701 has a service plan in Finland may serve to elevate the rank of search results known to be located in Finland. For example, IMSI data indicating a Finnish origin of user 1701 may result in John Smith's (the search term) that have Finnish connections being ranked higher in the presentation of disambiguated search terms.

Operation 2104 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including electronic serial number data corresponding to at least one communication device. For example, disambiguation unit 1702 and/or device-identifier data acceptor module 1712 may associate the at least one search term with device-identifier data including electronic serial number data corresponding to at least one communication device. In one embodiment, disambiguation unit 1702 may associate a search term with an electronic serial number belonging to cell phone of a member of the same carrier network as the user 1701. In this embodiment, membership in the same carrier network may be a significant association that elevates the rank of that result as a disambiguated search term.

FIG. 22 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 22 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2202, 2204, 2206, and/or operation 2208.

Operation 2202 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account. For example, disambiguation unit 1702 may associate a mobile equipment identifier from a mobile phone's SIM card from a telecommunications carrier 220, the MEID corresponding to a billing account for a subscriber of a wireless service provided by the telecommunications carrier 220, for example, user 1701. That association may then in turn be used to associate a search term from user 1701 with results having connections to user 1701 and/or the carrier 220 associated with user 1701's billing account.

Operation 2204 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises at least one of a cable telecommunications billing account, a wireless telecommunications billing account, or a satellite telecommunications billing account. For example, disambiguation unit 1702 may associate the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises at least one of a cable telecommunications billing account, a wireless telecommunications billing account, or a satellite telecommunications billing account. For example, disambiguation unit 1702 may receive ancillary query data 1703 from user 1701 in the form of login status at a website, such as a Google home page. In one embodiment that login status may include data regarding a wireless telecommunications billing account, for example, for an Android mobile device. Such data may include name, address, or other personal information about user 1701 that may be used by disambiguation unit 1702 to make associations between a search term and the sphere of connections surrounding user 1701.

Operation 2206 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises an electronic payment account. For example, disambiguation unit 1702 may associate the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises an electronic payment account. For example, to continue the example above, disambiguation unit 1702 may receive ancillary query data 1703 from user 1701 in the form of login status at a website, such as a Google home page. In one embodiment that login status may include data regarding an electronic billing account, for example, Google Wallet. In another embodiment, disambiguation unit 1702 may receive ancillary query data 1703 from user 1701 in the form of a mobile device ID for an Android mobile phone from an Android app such as “Android Device ID” available for download from the Android Market. The Android mobile device ID for the mobile phone, for example derived from a wireless network socket, may correspond to a Google Checkout account for the subscriber of the wireless service to the mobile phone. As discussed above, such data may include name, address, or other personal information about user 1701 that may be used by disambiguation unit 1702 to make associations between a search term and the sphere of connections surrounding user 1701.

Operation 2208 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises an electronic payment account, and wherein the electronic payment account comprises at least one of a Google Checkout account, an Amazon Payments account, a PayPal account, or a mobile PayPal account. For example, disambiguation unit 1702 may associate the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one billing account, wherein the at least one billing account comprises an electronic payment account, and wherein the electronic payment account comprises at least one of a Google Checkout account, an Amazon Payments account, a PayPal account, or a mobile PayPal account. For example, disambiguation unit 1702 may receive ancillary query data 1703 from user 1701 in the form of login status at a website, such as Amazon.com. In one embodiment that login status may include data regarding an electronic billing account, for example, Amazon Payments. Such data may include name, address, purchase history, wishlist, or other personal information about user 1701 that may be used by disambiguation unit 1702 to make associations between a search term and the sphere of connections surrounding user 1701.

FIG. 23 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 23 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2300, 2302, and/or operation 2304.

Operation 2300 depicts disambiguating the at least one search term including associating the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one of a credit card account or a virtual account. For example, disambiguation unit 1702 may associate the at least one search term with device-identifier data including device-identifier data corresponding to at least one communication device that is linked to at least one of a credit card account or a virtual account. For example, a user's wireless device's service subscription may be linked to a user's credit card account.

Operation 2302 depicts disambiguating the at least one search term including associating the at least one search term with email data corresponding to at least one user of at least one communication device. For example, disambiguation unit 1702 may associate the at least one search term with email data corresponding to at least one user of at least one communication device. For example, network-participation identifier data acceptor module 1714 may accept an email address as network-participation identifier data corresponding to user 1701, the email address associated with a name and address on file with a telecommunications company with which the user has a service subscription. In a further example, for a searching user 1701 logged in to a gmail account, for example, disambiguation unit 1702 may have access to text in the emails of the personal mailbox of the user as a data source to search for associations with the search term.

Operation 2304 depicts disambiguating the at least one search term including associating the at least one search term with email data corresponding to at least one user of at least one communication device, wherein the email data comprises a simple mail transfer protocol-compatible email address or an email alias. In one example, disambiguation unit 1702 may associate the at least one search term with email data corresponding to at least one user of at least one communication device, wherein the email data comprises a simple mail transfer protocol-compatible email address or an email alias. For example, network-participation identifier data acceptor module 1714 may accept an email alias as network-participation identifier data corresponding to user 1701, the email alias associated with a name on file with a website with which the user has an account, such as hotmail.com.

FIG. 24 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 24 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2400, 2402, 2404, and/or operation 2406.

Operation 2400 depicts disambiguating the at least one search term including associating the at least one search term with network-participation identifier data associated with at least one of a user's social security number, a user's national identification card, a user's biometric measurement, a user's passport number, a user's tax identification number, a user's internet domain, or a user's authentication certificate. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with network-participation identifier data associated with at least one of a user's social security number, a user's national identification card, a user's biometric measurement, a user's passport number, a user's tax identification number, a user's internet domain, or a user's authentication certificate. As another example, network-participation identifier data acceptor module 1714 may accept a Facebook username or login status as network-participation identifier data, the username associated with a photograph of the user 1701 as image data or as a biometric measurement. In some embodiments, an image recognition system may be employed to associate an image with a specific user 1701 for biometric purposes. Identifying features of the user 1701, such as social network participation, national status, web presence indicators (e.g., blog authorship, personal web pages, or the like), may provide associations for disambiguation unit 1702 to use in presenting a disambiguated search term of high relevance to the user 1701.

As discussed above, as used herein, “network-participation identifier data” may refer to a signifier of belonging in a network, such as an email address; a username, such as a social networking user name; or other mark such as an image, number, or writing that signifies participation in a particular network.

Operation 2402 depicts disambiguating the at least one search term including associating the at least one search term with social networking data. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data. As another example, network-participation identifier data acceptor module 1714 may accept a Facebook username or login status as network-participation identifier data, the username associated with a photograph of the user 1701, interests of the user 1701, education of the user 1701, home town of the user 1701, current location of the user 1701 (e.g., city or street level location a la foursquare.com), or the like. As discussed above, identifying features of the user 1701 gleaned from social network participation may provide associations with a search term for disambiguation unit 1702 to use in presenting a disambiguated search term of high relevance to the user 1701. For example, searching for “Mike Jones” may result in disambiguation unit 1702 making an association with a Mike Jones who is a Google+ friend of a Google+ friend of the user 1701 logged in to Google+. That specific Mike Jones may then be presented as the first result in a list of disambiguated search terms. Details of the association may also be presented by disambiguation unit 1702 with the disambiguated search term so as to inform the user 1701 as to the association underlying the ranking, and the known attributes of that particular Mike Jones.

Operation 2404 depicts disambiguating the at least one search term including associating the at least one search term with social networking data, wherein the social networking data comprises at least one of a username, an @-tagged twitter handle, a corporate login, or a website uniform resource locator. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data, wherein the social networking data comprises at least one of a username, an @-tagged twitter handle, a corporate login, or a website uniform resource locator. As in the above Facebook example, network-participation identifier data acceptor module 1714 may accept a Facebook username as network-participation identifier data, the username associated with various attributes of the user 1701 as grist for making associations with the search term(s).

Operation 2406 depicts disambiguating the at least one search term including associating the at least one search term with social networking data, wherein the social networking data comprises at least one of Facebook data, Twitter data, or LinkedIn data. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data, wherein the social networking data comprises at least one of Facebook data, Twitter data, or LinkedIn data. For example, network-participation identifier data acceptor module 1714 may accept a LinkedIn username as network-participation identifier data, the username associated with various attributes of the user 1701 as a basis for making associations with the search term(s).

FIG. 25 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 25 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2502, 2504, and/or operation 2506.

Operation 2502 depicts disambiguating the at least one search term including associating the at least one search term with social networking data, wherein the social networking data comprises at least one of image data, constellation of social contact data, or user input data. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data, wherein the social networking data comprises at least one of image data, constellation of social contact data, or user input data. For example, network-participation identifier data acceptor module 1714 may accept a Facebook username as network-participation identifier data, the username associated with a constellation of friends of the user 1701, as a basis for making associations with the search term(s). As discussed above, a photograph of a user 1701 may be analyzed by image recognition technologies to identify a person having specific geographic, demographic, or other identifying characteristics.

Operation 2504 depicts disambiguating the at least one search term including associating the at least one search term with social networking data, wherein the social networking data comprises data accumulated from multiple sources. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data, wherein the social networking data comprises data accumulated from multiple sources. For example, network-participation identifier data acceptor module 1714 may accept a Google+ username and Twitter username as network-participation identifier data, the usernames associated with a constellation of Facebook friends and/or Twitter followers of the user 1701, as a basis for making associations with the search term(s). In this way, significant power of association (and resulting disambiguation) may be obtained by making associations based on common matches from multiple sources.

Operation 2506 depicts disambiguating the at least one search term including associating the at least one search term with social networking data, wherein the social networking data comprises at least one of data used to create additional data or data used to find additional data. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with social networking data, wherein the social networking data comprises at least one of data used to create additional data or data used to find additional data. For example, network-participation identifier data acceptor module 1714 may associate a search term with a website URL of a social networking site's videoconferencing or videochat feed as data (website URL) used to create additional data (streaming video of conference/chat/network participants). In another example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate a search term with a user image or alias that can be used to find other data, for example as a search term in an reverse-image query or a text query, respectively.

FIG. 26 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 26 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2600, 2602, and/or operation 2604.

Operation 2600 depicts disambiguating the at least one search term including associating the at least one search term with a real-world user associated with at least one communication device. In one example, disambiguation unit 1702 and/or ancillary query data acceptor module 1710 may associate the at least one search term with a real-world user associated with at least one communication device. For example, network-participation identifier data acceptor module 1714 may associate a search term with a real-world user 1701 on the basis of telecommunication carrier records and/or network account records, for example when a user 1701 performs a Google search from a Google phone while logged in to a personal Google page or Google+. In other embodiments, identity prediction methods may be employed to associate a search term with a real-world user, or to associate a user 1701 with a real-world user, as described below. In some embodiments, disambiguation unit 1702 may associate user 1701 with identifying data (e.g., device data or network participation data), and then associate the now-known attributes of the user 1701 with the search term in order to disambiguate the search term. Matches or associations derived in this way may provide significant power in disambiguation by disambiguation unit 1702, which may present such matches as highly ranked disambiguated search terms. This approach can also be done in a complementary way in reverse—disambiguation unit 1702 can associate a search term with device identification data and/or network participation data, which can then be cross-reference with known attributes of user 1701 to find commonalities indicative of relevance.

As another example, a Facebook username search term “Mike Jones” may be used by identity prediction module 122 to find the same username “Mike Jones” on a blog containing a real-world name “Michael B. Jones” and mobile phone number associated with the username and real-world name, the mobile phone number being assigned to an iPhone associated with the real-world user, whose “Mike Jones” username is within the same social network as the searching user 1701. This particular “Mike Jones” or “Michael B. Jones” may then be presented with the mobile phone number as a disambiguated search term to user 1701.

As described above, sources of data for associating a search term or user 1701 with network-participation data and/or a communication device may include, for example, information that is provided by the user. For example, social network, message boards, interne forums, and the like may contain a link between a username and a phone number, a real-world name, birth date, gender, age, or other identifying attribute. Private sources of data may also include information provided by the user, such as private social networks, ecommerce websites, or any websites to which a consumer provides sign-up information. Publicly available sources may contain unique consumer information, including for example, vehicle registration records, real estate records, driving records, voting records, political donations, health information, government related data, technographics, or any other on-line sources disclosing information about people. Examples of algorithms that may be employed to perform these associations can be found in U.S. Patent Application Publication 2010/0088313 “Data Source Attribution System,” hereby incorporated in its entirety by reference. See also U.S. Patent Application Publication 2010/0010993 “Distributed Personal Information Aggregator,” also hereby incorporated in its entirety by reference. See also web databases such as the Notable Names Database (NNDB) at http://www.nndb.com/ and similar public databases providing information about people.

Operation 2602 depicts disambiguating the at least one search term including associating the at least one search term with a real-world user associated with at least one communication device, wherein the associating the at least one search term with a real-world user associated with at least one communication device includes at least one of performing the association using identity prediction, performing the association using de-anonymization, or performing the association using real-name profiling. In one example, disambiguation unit 1702, de-anonymization module 244, real-name profiling module 246, and/or identity prediction module 122 may associate the at least one search term with a real-world user associated with at least one communication device, wherein the associating the at least one search term with a real-world user associated with at least one communication device includes at least one of performing the association using identity prediction, performing the association using de-anonymization, or performing the association using real-name profiling. For example, real-name profiling module 246 may associate a name as a search term with people having that name, including attributes that differentiate those of the same name, such as location, age, or any other distinguishing features. Those attributes may be cross-referenced with attributes of the user 1701 to identify commonalities that would indicate a connection and increased relevance for disambiguation.

Operation 2604 depicts disambiguating the at least one search term including associating the at least one search term with a real-world user associated with at least one communication device, wherein the associating the at least one search term with a real-world user associated with at least one communication device includes at least one of performing the association using web history tracking, performing the association using media content tracking, or performing the association using app data tracking. In one example, disambiguation unit 1702, web history tracking module 248, media content tracking module 250, app data tracking module 252, and/or identity prediction module 122 may associate the at least one search term with a real-world user associated with at least one communication device, wherein the associating the at least one search term with a real-world user associated with at least one communication device includes at least one of performing the association using web history tracking, performing the association using media content tracking, or performing the association using app data tracking.

For example, app data tracking module 252 may obtain and associate a user's device ID with a telecommunications carrier network. That network may then be used as a basis for finding a relevant search term, thereby narrowing the possibilities and disambiguating the search term. Alternatively, ancillary query data acceptor module 1710 may accept from Google the email address of a user 1701 associated with an Android phone and corresponding account with a wireless carrier (e.g., the user performing a Google search on an Android phone). In this example, app data tracking module 252 may match the email address with device ID from the phone, e.g., SIM data, by monitoring apps used by the user 1701 that provide this data, and then make the association between the email address and the phone. Additionally, web history tracking module 248 may search public databases for identifying features of a real-world, such as physical address or phone number associated with the user's email address, for example by searching department of motor vehicle records or real estate records. As discussed above, those attributes may be cross-referenced with attributes of the search term to identify commonalities that would indicate a connection and increased relevance for disambiguation.

FIG. 27 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 27 illustrates example embodiments where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2700, 2702, 2704, 2706, and/or operation 2708.

Operation 2700 depicts disambiguating the at least one search term including associating the at least one search term with network-participation identifier data and device-identifier data. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with network-participation identifier data and device-identifier data relating to a searching user 1701. For example, ancillary query data acceptor module 1710 may receive both device location data, for example via foursquare, and network participation data via Facebook for the user 1701. Disambiguation unit 1702 may then apply those data to the search term, “Mike Jones” for example, to find those “Mike Jones”es who are near the user's location and either in the Facebook social network or specifically connected to the searching user 1701 on Facebook or connected to a friend of the searching user 1701.

Operation 2702 depicts disambiguating the at least one search term including associating the at least one search term with data indicating the origin of input of the at least one search term. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with data indicating the origin of input of the at least one search term. For example, ancillary query data acceptor module 1710 may receive device location data, for example via foursquare, for the user 1701. Disambiguation unit 1702 may then apply those data to the search term, “MJones” for example, to find those “MJones”es who are near the user's foursquare location.

Operation 2704 depicts disambiguating the at least one search term including associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term includes internet protocol address data. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term includes internet protocol address data. For example, ancillary query data acceptor module 1710 may receive device location data including IP address data, for example via a web browsing session on a desktop computer, for the user 1701. To the extent that an IP address provides an indication of geographic location, disambiguation unit 1702 may then apply those location data to the search term, “MJones” for example, to find those “MJones”es who are near the user's computer's location. Disambiguation unit 1702 may employ geolocation software to identify, based on a user's IP address, the country (including down to the city and ZIP code level), organization, or user the IP address has been assigned to. Other methods include examination of a MAC address, image metadata, or available credit card information.

Operation 2706 depicts disambiguating the at least one search term including associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term is derived from at least one of cellular phone tower proximity, Wi-Fi use, user-entered location data, or proximity to at least one other device. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term is derived from at least one of cellular phone tower proximity, Wi-Fi use, user-entered location data, or proximity to at least one other device. For example, ancillary query data acceptor module 1710 may receive device location data from a Wi-Fi smart phone of a searching user 1701 by accessing a database that contains location information for the Wi-Fi network, and then ancillary query data acceptor module 1710 may use that location data to disambiguate the search term relative to the user's location.

Operation 2708 depicts disambiguating the at least one search term including associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term is derived from at least one of a detected vehicle use, a detected user activity, or a detected user location. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with data indicating the origin of input of the at least one search term, wherein the data indicating the origin of input of the at least one search term is derived from at least one of a detected vehicle use, a detected user activity, or a detected user location. For example, ancillary query data acceptor module 1710 may receive device location data from detected automobile use, based on, for example, last known location and predicted range of travel of an automobile equipped with gps tracking, for example. In another example, ancillary query data acceptor module 1710 may receive or deduce location data from a detected user activity, for example, checking in with foursquare at a specific location or searching for driving directions in a web browser, respectively.

FIG. 28 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 28 illustrates an example embodiment where the disambiguating operation 1820 may include at least one additional operation. Additional operations may include operation 2800.

Operation 2800 depicts disambiguating the at least one search term including associating the at least one search term with at least one entry in an inter-service-provider directory of device-associated users. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may disambiguate the at least one search term by associating the at least one search term with at least one entry in an inter-service-provider directory of device-associated users. For example as discussed above, a telecommunications carrier, social network, or other entity may create a directory of device-associated users across all carriers that functions as a modern-day white pages for communication devices regardless of carrier. This “inter-service-provider directory” may, for example and as discussed above, be based on SIM data identifying a user's mobile phone, and at least partly based on a user's participation in a wireless network. Attributes associated with entries in such a directory may be used by disambiguation unit 1704 to associate a search term with a phone number in the instant application, for presenting to the user 1701 as a disambiguated search term.

As discussed above, if many wireless carriers agree to share their unique identifier lists and keep them in the same format for use as a global directory of mobile phone users, a comprehensive “white pages” of communications device users becomes possible, across potentially all service providers. Such a directory could also be keyed to social networking data such as username or user image, such that, for example, Facebook users could easily find each other's device contact information and possibly location information. Inclusion of users' device information in such a directory could be done on an opt-in basis.

As used herein, a unique identifier based on a device-identifier and a network-participant identifier may be keyed to that underlying data. That is, having the unique identifier corresponding to specific device data and specific network-participation identifier data associated with a verified real-world user associated with the at least one communication device will permit the creator of the unique identifier to use it to call up the specific device data and specific network participation identifier data. This may allow, for example, a telecommunications carrier to disambiguate one user from another having similar or identical network participation identifier data. This can be done on the basis of different device identifier data for the two users with similar or identical network participation identifier data, for example.

FIG. 29 illustrates alternative embodiments of the example operational flow 1800 of FIG. 18. FIG. 29 illustrates an example embodiment where the presenting operation 1830 may include at least one additional operation. Additional operations may include operation 2900.

Operation 2900 depicts presenting a disambiguated search term at least partly based on both network-participation identifier data and device-identifier data. In one example, disambiguation unit 1702, ancillary query data acceptor module 1710, and/or identity prediction module 122 may present a disambiguated search term at least partly based on both network-participation identifier data and device-identifier data. For example, ancillary query data acceptor module 1710 may associate a user's IP address-derived location data and social network participation data with a search term to narrow the list of possible matches for the search term, filter out likely mismatches, and rank results in order of relevance.

FIG. 30 illustrates a partial view of an example article of manufacture 3000 that includes a computer program 3004 for executing a computer process on a computing device. An embodiment of the example article of manufacture 3000 is provided including a signal bearing medium 3002, and may include one or more instructions for accepting at least one search term corresponding to at least one member of a network; one or more instructions for disambiguating the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and one or more instructions for presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. The one or more instructions may be, for example, computer executable and/or logic-implemented instructions. In one implementation, the signal-bearing medium 3002 may include a computer-readable medium 3006. In one implementation, the signal bearing medium 3002 may include a recordable medium 3008. In one implementation, the signal bearing medium 3002 may include a communications medium 3010.

FIG. 31 illustrates an example system 3100 in which embodiments may be implemented. The system 3100 includes a computing system environment. The system 3100 also illustrates a user 3112 using a device 3104, which is optionally shown as being in communication with a computing device 3102 by way of an optional coupling 3106. The optional coupling 3106 may represent a local, wide-area, or peer-to-peer network, or may represent a bus that is internal to a computing device (e.g., in example embodiments in which the computing device 3102 is contained in whole or in part within the device 3104). A storage medium 3108 may be any computer storage media. In one embodiment, the computing device 3102 may include a virtual machine operating within another computing device. In an alternative embodiment, the computing device 3102 may include a virtual machine operating within a program running on a remote server.

The computing device 3102 includes computer-executable instructions 3110 that when executed on the computing device 3102 cause the computing device 3102 to (a) accept at least one search term corresponding to at least one member of a network; (b) disambiguate the at least one search term including associating the at least one search term with at least one of network-participation identifier data or device-identifier data; and (c) presenting a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. As referenced above and as shown in FIG. 31, in some examples, the computing device 3102 may optionally be contained in whole or in part within the device 3104.

In FIG. 31, then, the system 3100 includes at least one computing device (e.g., 3102 and/or 3104). The computer-executable instructions 3110 may be executed on one or more of the at least one computing device. For example, the computing device 3102 may implement the computer-executable instructions 3110 and output a result to (and/or receive data from) the computing device 3104. Since the computing device 3102 may be wholly or partially contained within the computing device 3104, the device 3104 also may be said to execute some or all of the computer-executable instructions 3110, in order to be caused to perform or implement, for example, various ones of the techniques described herein, or other techniques.

The device 3104 may include, for example, a portable computing device, workstation, or desktop computing device. In another example embodiment, the computing device 3102 is operable to communicate with the device 3104 associated with the user 3112 to receive information about the input from the user 3112 for performing data access and data processing, and present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data.

One skilled in the art will recognize that the herein described components (e.g., operations), devices, objects, and the discussion accompanying them are used as examples for the sake of conceptual clarity and that various configuration modifications are contemplated. Consequently, as used herein, the specific exemplars set forth and the accompanying discussion are intended to be representative of their more general classes. In general, use of any specific exemplar is intended to be representative of its class, and the non-inclusion of specific components (e.g., operations), devices, and objects should not be taken limiting.

Those skilled in the art will appreciate that the foregoing specific exemplary processes and/or devices and/or technologies are representative of more general processes and/or devices and/or technologies taught elsewhere herein, such as in the claims filed herewith and/or elsewhere in the present application.

Those having skill in the art will recognize that the state of the art has progressed to the point where there is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software can become significant) a design choice representing cost vs. efficiency tradeoffs. Those having skill in the art will appreciate that there are various vehicles by which processes and/or systems and/or other technologies described herein can be effected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; alternatively, if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware. Hence, there are several possible vehicles by which the processes and/or devices and/or other technologies described herein may be effected, none of which is inherently superior to the other in that any vehicle to be utilized is a choice dependent upon the context in which the vehicle will be deployed and the specific concerns (e.g., speed, flexibility, or predictability) of the implementer, any of which may vary. Those skilled in the art will recognize that optical aspects of implementations will typically employ optically-oriented hardware, software, and or firmware.

In some implementations described herein, logic and similar implementations may include software or other control structures. Electronic circuitry, for example, may have one or more paths of electrical current constructed and arranged to implement various functions as described herein. In some implementations, one or more media may be configured to bear a device-detectable implementation when such media hold or transmit a device detectable instructions operable to perform as described herein. In some variants, for example, implementations may include an update or modification of existing software or firmware, or of gate arrays or programmable hardware, such as by performing a reception of or a transmission of one or more instructions in relation to one or more operations described herein. Alternatively or additionally, in some variants, an implementation may include special-purpose hardware, software, firmware components, and/or general-purpose components executing or otherwise invoking special-purpose components. Specifications or other implementations may be transmitted by one or more instances of tangible transmission media as described herein, optionally by packet transmission or otherwise by passing through distributed media at various times.

Alternatively or additionally, implementations may include executing a special-purpose instruction sequence or invoking circuitry for enabling, triggering, coordinating, requesting, or otherwise causing one or more occurrences of virtually any functional operations described herein. In some variants, operational or other logical descriptions herein may be expressed as source code and compiled or otherwise invoked as an executable instruction sequence. In some contexts, for example, implementations may be provided, in whole or in part, by source code, such as C++, or other code sequences. In other implementations, source or other code implementation, using commercially available and/or techniques in the art, may be compiled//implemented/translated/converted into a high-level descriptor language (e.g., initially implementing described technologies in C or C++ programming language and thereafter converting the programming language implementation into a logic-synthesizable language implementation, a hardware description language implementation, a hardware design simulation implementation, and/or other such similar mode(s) of expression). For example, some or all of a logical expression (e.g., computer programming language implementation) may be manifested as a Verilog-type hardware description (e.g., via Hardware Description Language (HDL) and/or Very High Speed Integrated Circuit Hardware Descriptor Language (VHDL)) or other circuitry model which may then be used to create a physical implementation having hardware (e.g., an Application Specific Integrated Circuit). Those skilled in the art will recognize how to obtain, configure, and optimize suitable transmission or computational elements, material supplies, actuators, or other structures in light of these teachings.

The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link (e.g., transmitter, receiver, transmission logic, reception logic, etc.), etc.).

In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, and/or any combination thereof can be viewed as being composed of various types of “electrical circuitry.” Consequently, as used herein “electrical circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of memory (e.g., random access, flash, read only, etc.)), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, optical-electrical equipment, etc.). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.

Those skilled in the art will recognize that at least a portion of the devices and/or processes described herein can be integrated into a data processing system. Those having skill in the art will recognize that a data processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile or non-volatile memory, processors such as microprocessors or digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices (e.g., a touch pad, a touch screen, an antenna, etc.), and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A data processing system may be implemented utilizing suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.

Those skilled in the art will recognize that it is common within the art to implement devices and/or processes and/or systems, and thereafter use engineering and/or other practices to integrate such implemented devices and/or processes and/or systems into more comprehensive devices and/or processes and/or systems. That is, at least a portion of the devices and/or processes and/or systems described herein can be integrated into other devices and/or processes and/or systems via a reasonable amount of experimentation. Those having skill in the art will recognize that examples of such other devices and/or processes and/or systems might include—as appropriate to context and application—all or part of devices and/or processes and/or systems of (a) an air conveyance (e.g., an airplane, rocket, helicopter, etc.), (b) a ground conveyance (e.g., a car, truck, locomotive, tank, armored personnel carrier, etc.), (c) a building (e.g., a home, warehouse, office, etc.), (d) an appliance (e.g., a refrigerator, a washing machine, a dryer, etc.), (e) a communications system (e.g., a networked system, a telephone system, a Voice over IP system, etc.), (f) a business entity (e.g., an Internet Service Provider (ISP) entity such as Comcast Cable, Century Link, Southwestern Bell, etc.), or (g) a wired/wireless services entity (e.g., Sprint, Verizon, AT&T, etc.), etc.

In certain cases, use of a system or method may occur in a territory even if components are located outside the territory. For example, in a distributed computing context, use of a distributed computing system may occur in a territory even though parts of the system may be located outside of the territory (e.g., relay, server, processor, signal-bearing medium, transmitting computer, receiving computer, etc. located outside the territory).

A sale of a system or method may likewise occur in a territory even if components of the system or method are located and/or used outside the territory.

Further, implementation of at least part of a system for performing a method in one territory does not preclude use of the system in another territory.

All of the above U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in any Application Data Sheet are incorporated herein by reference, to the extent not inconsistent herewith.

The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable,” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components, and/or wirelessly interactable, and/or wirelessly interacting components, and/or logically interacting, and/or logically interactable components.

In some instances, one or more components may be referred to herein as “configured to,” “configured by,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that such terms (e.g. “configured to”) can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations are not expressly set forth herein for sake of clarity.

While particular aspects of the present subject matter described herein have been shown and described, it will be apparent to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from the subject matter described herein and its broader aspects and, therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of the subject matter described herein. It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flows are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. 

1-76. (canceled)
 77. A computer-implemented system comprising: a primary query data acceptor module configured to accept at least one search term corresponding to at least one member of a network; and a disambiguation unit configured to disambiguate the at least one search term including circuitry for associating the at least one search term with at least one of network-participation identifier data or device-identifier data; wherein the disambiguation unit is configured to present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data.
 78. The system of claim 77 wherein the primary query data acceptor module further comprises at least one of a name query acceptor module or a number query acceptor module.
 79. The system of claim 77 wherein the disambiguation unit further comprises an ancillary query data acceptor module.
 80. The system of claim 79 wherein the ancillary query data acceptor module further comprises at least one of a device-identifier acceptor module, a network-participation identifier data acceptor module, or a search locus data acceptor module.
 81. The system of claim 77 wherein the disambiguation unit further comprises an identity prediction module.
 82. The system of claim 81 wherein the identity prediction module further comprises at least one of a de-anonymization module, a real-name profiling module, a web history tracking module, or an app data tracking module.
 83. The system of claim 77 wherein the disambiguation unit further comprises a subscriber database.
 84. The system of claim 83 wherein the subscriber database includes a cross-carrier subscriber database.
 85. The system of claim 77 wherein the disambiguation unit further comprises at least one of filter logic, ranking logic, or correction logic.
 86. The system of claim 85 wherein the filter logic is configured to filter search results based on at least one of shared network participation, shared location, or shared device attribute.
 87. The system of claim 85 wherein the ranking logic is configured to rank search results based on at least one of degree of shared network participation, degree of shared location, or degree of shared device attribute.
 88. The system of claim 85 wherein the correction logic is configured to detect at least one possible error in a search term entered by a user at least partly based on at least one of the network-participation identifier data or the device-identifier data.
 89. The system of claim 88 wherein the correction logic is configured to suggest at least one alternative to a search term entered by a user at least partly based on at least one of the network-participation identifier data or the device-identifier data.
 90. A computer-implemented system comprising: a primary query data acceptor module configured to accept at a user interface at least one search term corresponding to at least one member of a network; and a disambiguation unit configured to disambiguate the at least one search term including circuitry for associating the at least one search term with at least one of network-participation identifier data or device-identifier data; wherein the disambiguation unit is configured to present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data.
 91. A computer-implemented system comprising: a primary query data acceptor module operative on a computational platform, wherein the primary query data acceptor module is configured to accept at least one search term corresponding to at least one member of a network; and a disambiguation unit operative on a computational platform, wherein the disambiguation unit is configured to disambiguate the at least one search term including circuitry for associating the at least one search term with at least one of network-participation identifier data or device-identifier data; wherein the disambiguation unit is configured to present a disambiguated search term at least partly based on at least one of the network-participation identifier data or the device-identifier data. 